| Statistical Inference with Neural Network Imputation for Item Non-response 세미나 | |||
| 작성일 | 2021-10-22 | 조회수 | 171 |
|---|---|---|---|
| 첨부파일 | |||
|
제목 : Statistical Inference with Neural Network Imputation for Item Non-response
일시 : 2021.06.17(목)
연사 : 미국 아이오아 대학교 통계학과 김재광 교수
초록 : We consider the problem of nonparametric imputatuin using a neural network model. Neural network models can capture complex nonlinear trends and interaction effects, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation, including variance estimation, is challenging because the basis for function estimation is estimated rather than known. In this paper, we tackle the problem of statistical inference with neural network imputation by treationg the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the lineariztion method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe non-linearity and complicated interactions. |
|||
| 다음 | 딥러닝 오토인코드 모형에 대한 다단계 가능도 방법론 연구 세미나 |
|---|---|
| 이전 | 이전 게시글이 없습니다. |